Surveying expression level polymorphism and single-feature polymorphism in near-isogenic wheat lines differing for the Yr5 stripe rust resistance locus

  • Tristan E. Coram
  • Matthew L. Settles
  • Meinan Wang
  • Xianming Chen
Original Paper

Abstract

DNA polymorphisms are valuable for several applications including genotyping, molecular mapping and marker-assisted selection. The 55 K Affymetrix Wheat GeneChip was used to survey expression level polymorphisms (ELPs) and single-feature polymorphisms (SFPs) between two near-isogenic wheat genotypes (BC7:F4) that differ for the Yr5 stripe rust resistance locus, with the objective of developing genetic markers linked to Yr5. Ninety-one probe sets showing ELPs and 118 SFP-containing probe sets were identified between isolines, of which just nine ELP probe sets also contained SFPs. The proportion of the transcriptome estimated to be variable between isolines from this analysis was 0.30% for the ELPs and 0.39% for the SFPs, which was highly similar to the theoretical genome difference between isolines of ~0.39%. Using wheat-rice synteny, both ELPs and SFPs mainly clustered on long arms of rice chromosomes four and seven, which are syntenous to wheat chromosomes 2L (Yr5 locus) and 2S, respectively. The strong physical correlation between the two types of polymorphism indicated that the ELPs may be regulated by cis-acting DNA polymorphisms. Twenty SFPs homologous to rice 4L were used to develop additional genetic markers for Yr5. Physical mapping of the probe sets containing SFPs to wheat chromosomes identified nine on the target chromosome 2BL, thus wheat-rice synteny greatly enhanced the selection of SFPs that were located on the desired wheat chromosome. Of these nine, four were converted into polymorphic cleaved amplified polymorphic sequence (CAPS) markers between Yr5 and yr5 isolines, and one was mapped within 5.3 cM of the Yr5 locus. This study represents the first array-based polymorphism survey in near-isogenic genotypes, and the results are applied to an agriculturally important trait.

Supplementary material

122_2008_784_MOESM1_ESM.doc (1.8 mb)
Normalized expression and residual plots for the 9 expression level polymorphism (ELP) probe sets that also contained single-feature polymorphisms (SFPs) detected between the Yr5 and yr5 wheat isolines. The ‘normalized data’ plot shows robust robust multi-array average (RMA) background corrected and quantile-normalized expression log intensity values (y-axis) for each probe (x-axis) in the probe set. Horizontal lines indicate the expression summary value calculated for the probe set and vertical lines indicate the position of called SFPs. The ‘residual data’ plot shows the residuals after removing the expression effect that was used to detect SFPs. Vertical lines indicate positions of called SFPs. In both plots the black lines represent Yr5 GeneChip data and red lines represent yr5 data. Each figure is labeled with the probe set ID and also the result after visual inspection. (DOC 1879 kb)
122_2008_784_MOESM2_ESM.doc (189 kb)
Expression level polymorphisms (ELPs) detected between the Yr5 and yr5 wheat isolines, where ‘Isoline’ indicates the isoline with the significantly higher expression, and ‘Condition’ refers to Puccinia striiformis f. sp. tritici-inoculation (Pst), mock-inoculation (Mock) or both treatments (Both). ‘Rice chromosome’ indicates the rice chromosome to which each probe set had the highest sequence homology, if significant. Functional categories were based on the Munich Information Center for Protein Sequences classifications and putative function shows the best significant BLASTX database hit from HarvEST. NA indicates no significant homology. (DOC 189 kb)
122_2008_784_MOESM3_ESM.doc (582 kb)
Significant single-feature polymorphisms (SFPs) detected between the Yr5 and yr5 wheat isolines after applying significance analysis of microarrays (SAM). The sign of the d value indicates which isoline was polymorphic with regard to the reference GeneChip oligo (positive values predicted SFPs in Yr5 and negative values in yr5). ‘stdev’ refers to standard deviation of the d value, ‘rawp’ is the P value, ‘q.value’ is the adjusted P value after multiple testing correction, and ‘R.fold’ is the fold change between isolines using yr5 data as the reference. (DOC 582 kb)
122_2008_784_MOESM4_ESM.doc (160 kb)
Single-feature polymorphisms (SFPs) detected between the Yr5 and yr5 wheat isolines, where ‘Probes’ refers to the number of SFP probes within the probe set. ‘Rice chromosome’ indicates the rice chromosome to which each probe set had the highest sequence homology, if significant. Functional categories were based on the Munich Information Center for Protein Sequences classifications and putative function shows the best significant non-redundant BLASTX database hit from HarvEST. Probe sets marked with an asterisk were also identified as expression level polymorphisms (ELPs). NA indicates no significant homology. (DOC 159 kb)

References

  1. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300Google Scholar
  2. Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics 19:185–193PubMedCrossRefGoogle Scholar
  3. Borevitz JO, Liang D, Plouffe D, Chang HS, Zhu T, Weigel D, Berry CC, Winzeler E, Chory J (2003) Large-scale identification of single-feature polymorphisms in complex genomes genome res 13:513–523Google Scholar
  4. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FP, Kim IF, Markowitz V, Matesse JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME)-towards standards for microarray data. Nat Genet 29:365–371PubMedCrossRefGoogle Scholar
  5. Caicedo AL, Stinchcombe JR, Olsen KM, Schmitt J, Purugganan MD (2004) Epistatic interaction between Arabidopsis FRI and FLC flowering time genes generates a latitudinal cline in a life history trait. Proc Natl Acad Sci USA 101:15670–15675PubMedCrossRefGoogle Scholar
  6. Chen XM, Line RF, Shi ZX, Leung H (1998) Genetics of wheat resistance to stripe rust. In: Slinkard A (ed) 9th international wheat genetics symposium. University Extension Press, Saskatoon, pp 237–239Google Scholar
  7. Chen XM, Soria MA, Yan GP, Sun J, Dubcovsky J (2003) Development of sequence tagged site and cleaved amplified polymorphic sequence markers for wheat stripe rust resistance gene Yr5. Crop Sci 43:2058–2064Google Scholar
  8. Chen WJ, Chang SH, Hudson ME, Kwan W-K, Li J, Estes B, Knoll D, Shi L, Zhu T (2005) Contribution of transcriptional regulation to natural variations in Arabidopsis. Genome Biol 6:R32PubMedCrossRefGoogle Scholar
  9. Cong B, Liu JP, Tanksley S (2002) Natural alleles at a tomato fruit size quantitative trait locus differ by heterochronic regulatory mutations. Proc Natl Acad Sci USA 99:13606–13611PubMedCrossRefGoogle Scholar
  10. Coram TE, Wang MN, Chen XM (2008) Transcriptome analysis of the wheat-Puccinia striiformis f sp. tritici interaction. Mol Plant Pathol 9:157–169CrossRefPubMedGoogle Scholar
  11. Cui X, Xu J, Asghar R, Condamine P, Svensson JT, Wanamaker S, Stein N, Roose M, Close TJ (2005) Detecting single-feature polymorphisms using oligonucleotide arrays and robustified projection pursuit. Bioinformatics 21:3852–3858PubMedCrossRefGoogle Scholar
  12. Doerge RW (2002) Mapping and analysis of quantitative trait loci in experimental populations. Nat Rev Genet 3:43–52PubMedCrossRefGoogle Scholar
  13. Gassmann W, Hinsch ME, Staskawicz BJ (1999) The Arabidopsis RPS4 bacterial-resistance gene is a member of the TIR-NBS-LRR family of disease-resistance genes. Plant J 20:265–277PubMedCrossRefGoogle Scholar
  14. Gentleman R, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80PubMedCrossRefGoogle Scholar
  15. Grant MR, Godiard L, Straube E, Ashfield T, Lewald J, Sattler A, Innes RW, Dangl JL (1995) Structure of the Arabidopsis RPM1 gene enabling dual specificity disease resistance. Science 269:843–846PubMedCrossRefGoogle Scholar
  16. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of affymetrix GeneChip probe level data. Nucleic Acids Res 31:e15PubMedCrossRefGoogle Scholar
  17. Jaccoud D, Peng K, Feinstein D, Kilian A (2001) Diversity arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Res 29:e25PubMedCrossRefGoogle Scholar
  18. Jayasinghe R, Kong S, Coram TE, Kaganovitch J, Xue CCL, Li CG, Pang ECK (2007) Construction and validation of a prototype microarray for efficient and high-throughput genotyping of angiosperms. Plant Biotechnol J 5:282–289PubMedCrossRefGoogle Scholar
  19. Jordan MC, Somers DJ, Banks TW (2007) Identifying regions of the wheat genome controlling seed development by mapping expression quantitative trait loci. Plant Biotechnol J 5:442–453PubMedCrossRefGoogle Scholar
  20. Kliebenstein DJ, Lambrix V, Reichelt M, Gershenzon J, Mitchell-Olds T (2001) Gene duplication and the diversification of secondary metabolism: side chain modification of glucosinolates in Arabidopsis thaliana. Plant Cell 13:681–693PubMedCrossRefGoogle Scholar
  21. Kliebenstein DJ, West MA, Van Leeuwen H, Kim K, Doerge RW, Michelmore RW, St. Clair DA (2006a) Genomic survey of gene expression diversity in Arabidopsis thaliana. Genetics 172:1179–1189PubMedCrossRefGoogle Scholar
  22. Kliebenstein DJ, West MA, Van Leeuwen H, Loudet O, Doerge RW, St. Clair DA (2006b) Identification of QTLs controlling gene expression networks defined a priori. BMC Bioinformatics 7:308PubMedCrossRefGoogle Scholar
  23. Lambrix V, Reichelt M, Mitchell-Olds T, Kliebenstein DJ, Gershenzon J (2001) The Arabidopsis epithiospecifier protein promotes the hydrolysis of glucosinolates to nitriles and influences Trichoplusia ni herbivory. Plant Cell 13:2793–2807PubMedCrossRefGoogle Scholar
  24. Law CN (1976) Genetic control of yellow rust resistance in T. spelta album. Plant Breeding Institute, Cambridge, Annual Report 1975, pp 108–109Google Scholar
  25. Ling P, Chen XM (2005) Construction of a hexaploid wheat (Triticum aestivum L.) bacterial artificial chromosome library for cloning genes for stripe rust resistance. Genome 48:1028–1036PubMedCrossRefGoogle Scholar
  26. Lupton FGH, Macer RCF (1962) Inheritance of resistance to yellow rust (Puccinia glumarum Erikss & Henn) in seven varieties of wheat. Trans Br Mycol Soc 45:21–45CrossRefGoogle Scholar
  27. Macer RCF (1966) The formal and monosomic genetic analysis of stripe rust (Puccinia striiformis) resistance in wheat. In: Mackey J (ed) 2nd international wheat genetics symposium. Hereditas Suppl, Lund, pp 127–142Google Scholar
  28. McIntosh RA, Hart GE, Devos KM, Gale MD, Rogers WJ (1998) Catalogue of gene symbols for wheat. In: Slinkard A (ed) 9th international wheat genetics symposium. University Extension Press, Saskatoon, pp 1–235Google Scholar
  29. Qi LL, Echalier B, Chao S, Lazo GR, Butler GE, Anderson OD, Akhunov ED, Dvorak J, Linkiewicz AM, Ratnasiri A, Dubcovsky J, Bermudez-Kandianis CE, Greene RA, Kantety R, La Rota CM, Munkvold JD, Sorrells SF, Sorrells ME, Dilbirligi M, Sidhu D, Erayman M, Randhawa HS, Sandhu D, Bondareva SN, Gill KS, Mahmoud AA, Ma X-F, Gustafson JP, Miftahudin, Conley EJ, Nduati V, Gonzalez-Hernandez JL, Anderson JA, Peng JH, Lapitan NLV, Hossain KG, Kalavacharla V, Kianian SF, Pathan MS, Zhang DS, Nguyen HT, Choi D-W, Fenton RD, Close TJ, McGuire PE, Qualset CO, Gill BS (2004) A chromosome bin map of 16, 000 expressed sequence tag loci and distribution of genes among the three genomes of polyploid wheat. Genetics 168:701–712PubMedCrossRefGoogle Scholar
  30. Rostoks N, Borevitz JO, Hedley PE, Russell J, Mudie S, Morris J, Cardle L, Marshall DF, Waugh R (2005) Single-feature polymorphism discovery in the barley transcriptome. Genome Biol 6:R54PubMedCrossRefGoogle Scholar
  31. Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G, Linsley PS, Mao M, Stoughton RB, Friend SH (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297–302PubMedCrossRefGoogle Scholar
  32. Shen L, Gong J, Caldo RA, Nettleton D, Cook D, Wise RP, Dickerson JA (2005) BarleyBase—an expression profiling database for plant genomics. Nucleic Acids Res 33:D614–D618PubMedCrossRefGoogle Scholar
  33. Smith PH, Hadfield J, Hart NJ, Koebner RMD, Boyd LA (2007) STS markers for the wheat yellow rust resistance gene Yr5 suggest a NBS-LLR-type resistance gene cluster. Genome 50:259–265PubMedCrossRefGoogle Scholar
  34. Smyth GK (2005) Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W (eds) Bioinformatics and computational biology solutions using r and bioconductor. Springer, New York, pp 397–420CrossRefGoogle Scholar
  35. Sorrells ME, La Rota M, Bermudez-Kandianis CE, Greene RA, Kantety R, Munkvold JD, Miftahudin, Mahmoud A, Ma X, Gustafson PJ, Qi LL, Echalier B, Gill BS, Matthews DE, Lazo GR, Chao S, Anderson OD, Edwards H, Linkiewicz AM, Dubcovsky J, Akhunov ED, Dvorak J, Zhang D, Nguyen HT, Peng J, Lapitan NLV, Gonzalez-Hernandez JL, Anderson JA, Hossain K, Kalavacharla V, Kianian SF, Choi DW, Close TJ, Dilbirligi M, Gill KS, Steber C, Walker-Simmons MK, McGuire PE, Qualset CO (2003) Comparative DNA sequence analysis of wheat and rice genomes. Genome Res 13:1818–1827PubMedGoogle Scholar
  36. R Development Core Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  37. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98:5116–5121PubMedCrossRefGoogle Scholar
  38. Van Leeuwen H, Kliebenstein DJ, West MA, Kim K, Van Poecke R, Katagiri F, Michelmore RW, Doerge RW, St. Clair DA (2007) Natural variation among Arabidopsis thaliana accessions for transcriptome response to exogenous salicylic acid. Plant Cell 19:2099–2110PubMedCrossRefGoogle Scholar
  39. Walia H, Wilson C, Condamine P, Ismail AM, Xu J, Cui X, Close TJ (2007) Array-based genotyping and expression analysis of barley cv Maythorpe and Golden Promise. BMC Genomics 8:87PubMedCrossRefGoogle Scholar
  40. Wang RL, Stec A, Hey J, Lukens L, Doebley J (1999) The limits of selection during maize domestication. Nature 398:236–239PubMedCrossRefGoogle Scholar
  41. Wellings CR, Singh RP, McIntosh RA, Pretorius ZA (2004) The development and application of near isogenic lines for the wheat stripe (yellow) rust pathosystem. 11th international cereal rusts and powdery mildew conference. John Innes Centre, Norwich, p 39Google Scholar
  42. West MA, van Leeuwen H, Kozik A, Kliebenstein DJ, Doerge RW, St. Clair DA, Michelmore RW (2006) High-density haplotyping with microarray-based expression and single feature polymorphism markers in Arabidopsis. Genome Res 16:787–795PubMedCrossRefGoogle Scholar
  43. West MA, Kim K, Kliebenstein DJ, Van Leeuwen H, Michelmore RW, Doerge RW, St. Clair DA (2007) Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis. Genetics 175:1441–1450PubMedCrossRefGoogle Scholar
  44. Yan GP, Chen XM, Line RF, Wellings CR (2003) Resistance gene-analog polymorphism markers co-segregating with the Yr5 gene for resistance to wheat stripe rust. Theor Appl Genet 106:636–643PubMedGoogle Scholar
  45. Yuan Q, Ouyang S, Liu J, Suh B, Cheung F, Sultana R, Lee D, Quackenbush J, Buell CR (2003) The TIGR rice genome annotation resource: annotating the rice genome and creating resources for plant biologists. Nucleic Acids Res 31:229–233PubMedCrossRefGoogle Scholar
  46. Zhu T, Salmeron JM (2007) High-definition genome profiling for genetic marker discovery. Trends Plant Sci 12:196–202PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Tristan E. Coram
    • 1
    • 2
  • Matthew L. Settles
    • 3
  • Meinan Wang
    • 2
  • Xianming Chen
    • 1
    • 2
  1. 1.US Department of AgricultureAgricultural Research Service, Wheat Genetics, Quality, Physiology and Disease Research UnitPullmanUSA
  2. 2.Department of Plant PathologyWashington State UniversityPullmanUSA
  3. 3.Department of Molecular BiosciencesWashington State UniversityPullmanUSA

Personalised recommendations